Alexandra Meliou

Alexandra Meliou

Assistant Professor

College of Information and Computer Science

140 Governors Drive

University of Massachusetts

Amherst, MA 01003-9264 USA

Office: 330
Phone: +1-413-545-3788
Fax: +1-413-545-1249


My research interests are in the area of data and information management, with an emphasis on provenance, causality, and reverse data management. Data is critical in almost every aspect of society, including education, technology, healthcare, economy, and science. Poor understanding and handling of data, data biases, poor data quality, and errors in data-driven processes are detrimental in all domains that rely on data. The goal of my research is to target these particular challenges, to develop tools that improve our understanding of data and facilitate the diagnosis of errors, and to extend the capabilities of modern database systems to support complex decisions and strategy planning queries.

Prospective Students
show more

Current Projects

Data diagnosis Data X-Ray: Diagnosing errors in data systems
Poor data quality is estimated to cost the US economy more than $600 billion per year and erroneous price data in retail databases alone cost the US consumers $2.5 billion each year. While existing data cleaning techniques can be quite effective at purging datasets of errors, they disregard the fact that a lot of errors are systematic, inherent to the process that produces the data, and thus will keep occurring unless the problem is corrected at its source. In contrast to traditional data cleaning, in this project we focus on data diagnosis: explaining where and how the errors happen in a data generative process.
Collaboration: Google Research

Causality PackageBuilder: supporting queries for packages
Traditional database queries follow a simple paradigm: they define constraints, in the form of selection predicates, that each tuple in the result must satisfy. This paradigm is undoubtedly expressive and powerful. Nevertheless, it often proves insufficient to model the complex reasoning required by the evolving information needs of modern applications. In this proposal, we investigate a paradigm shift dictated by scenarios that require a collection of result tuples to satisfy constraints collectively, rather than individually.
Collaboration: NYU Abu Dhabi

Tiresias The Tiresias System
The goal of this project is to seamlessly integrate databases with constrained problem solving in a fully-fledged system. We are building a system that allows the user to specify an optimization problem over their data declaratively. The system then translates the declarative input into a mixed integer program that is sent to a dedicated solver.

Causality Causality in Databases
When queries return unexpected results, users require explanations for their observations. In this project we explore what constitutes a cause for a query answer, or non-answer, and augment databases with support for causal queries. We demonstrate how causality can be used to provide explanations, as well as identify and correct data errors in a process called post-factum data cleaning.
Collaboration: University of Washington, CMU

RDM Reverse Data Management
Reverse Data Management encompasses an array of problems in database research where an action needs to be performed on the input data, on behalf of desired outcomes in the output data. Some examples include updates through views, data generation, data cleaning and repair. Today, as increasingly more of the available data is derived from other data, there is an increased need to be able to modify the input in order to achieve a desired effect on the output, motivating a systematic study of RDM.

Funding sponsors